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2022
Lauscher, A., Wachsmuth, H., Gurevych, I., & Glavaš, G. (2022). On the Role of Knowledge in Computational Argumentation. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2107.00281
Lauscher, A., Wachsmuth, H., Gurevych, I., & Glavaš, G. (2022). Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation. Transactions of the Association for Computational Linguistics, 10(10), 1392-1422. https://doi.org/10.1162/tacl_a_00525
Lindauer, M., Zela, A., Stoll, D. O., Ferreira, F., Hutter, F., & Nierhoff, T. (2022). METHOD AND DEVICE FOR CREATING A SYSTEM FOR THE AUTOMATED CREATION OF MACHINE LEARNING SYSTEMS. (Patent Nr. US2022012636).
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., & Hutter, F. (2022). SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research, 2022(23). https://arxiv.org/abs/2109.09831
Lindauer, M., Zela, A., Stoll, D., Ferreira, F., Hutter, F., & Nierhoff, T. (2022). Verfahren und Vorrichtung zum Erstellen eines Systems zum automatisierten Erstellen von maschinellen Lernsystemen. (Patent Nr. DE102020208671).
Mallik, N., Hvarfner, C., Stoll, D., Janowski, M., Bergman, E., Lindauer, M. T., Nardi, L., & Hutter, F. (2022). PriorBand: HyperBand + Human Expert Knowledge. In 2022 NeurIPS Workshop on Meta Learning (MetaLearn) https://openreview.net/forum?id=ds21dwfBBH
Mohan, A., Ruhkopf, T., & Lindauer, M. (2022). Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML) Vorabveröffentlichung online. https://arxiv.org/abs/2206.03130
Moosbauer, J., Casalicchio, G., Lindauer, M., & Bischl, B. (2022). Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2206.05447
Parker-Holder, J., Rajan, R., Song, X., Biedenkapp, A., Miao, Y., Eimer, T., Zhang, B., Nguyen, V., Calandra, R., Faust, A., Hutter, F., & Lindauer, M. (2022). Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. Journal of Artificial Intelligence Research, 74(74), 517-568. https://doi.org/10.48550/arXiv.2201.03916, https://doi.org/10.1613/jair.1.13596
Schede, E., Brandt, J., Tornede, A., Wever, M., Bengs, V., Hüllermeier, E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration. Journal of Artificial Intelligence Research, 75, 425-487. https://doi.org/10.1613/jair.1.13676
Sengupta, M., Alshomary, M., & Wachsmuth, H. (2022). Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. In Proceedings of the 2022 Workshop on Figurative Language Processing (S. 137-142). Association for Computational Linguistics (ACL).
Spliethöver, M., Keiff, M., & Wachsmuth, H. (2022). No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media. In Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) (S. 2081-2093). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.152
Stahl, M., Spliethöver, M., & Wachsmuth, H. (2022). To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (S. 39-51). (NLPCSS 2022 - 5th Workshop on Natural Language Processing and Computational Social Science ,NLP+CSS, Held at the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022). Association for Computational Linguistics (ACL). https://aclanthology.org/2022.nlpcss-1.6/
Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (S. 10370-10380) https://ojs.aaai.org/index.php/AAAI/article/view/21279
Wachsmuth, H., & Alshomary, M. (2022). "Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain. In Proceedings of the 29th International Conference on Computational Linguistics (S. 344 - 354). International Committee on Computational Linguistics. https://doi.org/10.48550/arXiv.2209.02508
Wachsmuth, H., & Alshomary, M. (2022). “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations. Proceedings - International Conference on Computational Linguistics, COLING, 29(1), 344-354.
2021
Ajjour, Y., Al-Khatib, K., Cimiano, P., El Baff, R., Ell, B., Stein, B., & Wachsmuth, H. (2021). Preface. In Same Side Stance Classification Shared Task 2019: Proceedings of the Same Side Stance Classification Shared Task organized as a part of the 6th Workshop on Argument Mining (ArgMining 2019) and co-located with the the 57th Annual Meeting of the Association for Computational Linguistics (ACL19) (CEUR Workshop Proceedings; Band 2921). https://ceur-ws.org/Vol-2921/xpreface.pdf
Ajjour, Y., Al-Khatib, K., Cimiano, P., Baff, R. E., Ell, B., Stein, B., & Wachsmuth, H. (Hrsg.) (2021). Same Side Stance Classification Shared Task 2019. (CEUR Workshop Proceedings; Band 2921). http://ceur-ws.org/Vol-2921/
Al-Khatib, K., Trautner, L., Wachsmuth, H., Hou, Y., & Stein, B. (2021). Employing argumentation knowledge graphs for neural argument generation. In ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference (S. 4744-4754). Association for Computational Linguistics (ACL). https://aclanthology.org/2021.acl-long.366.pdf
Alshomary, M., Chen, W. F., Gurcke, T., & Wachsmuth, H. (2021). Belief-based Generation of Argumentative Claims. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (S. 224-233). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2101.09765, https://doi.org/10.18653/v1/2021.eacl-main.17
Alshomary, M., Syed, S., Dhar, A., Potthast, M., & Wachsmuth, H. (2021). Counter-Argument Generation by Attacking Weak Premises: Counter-Argument Generation by Attacking Weak Premises. In C. Zong, F. Xia, W. Li, & R. Navigli (Hrsg.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (S. 1816-1827). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-acl.159
Alshomary, M., Gurke, T., Syed, S., Heinisch, P., Spliethöver, M., Cimiano, P., Potthast, M., & Wachsmuth, H. (2021). Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In Proceedings of The 8th Workshop on Argument Mining, (S. 184-189). Association for Computational Linguistics (ACL). https://aclanthology.org/2021.argmining-1.19.pdf
Alshomary, M., & Wachsmuth, H. (2021). Toward audience-aware argument generation. Patterns, 2(6), Artikel 100253. https://doi.org/10.1016/j.patter.2021.100253
Barrow, J., Jain, R., Lipka, N., Dernoncourt, F., Morariu, V. I., Manjunatha, V., Oard, D. W., Resnik, P., & Wachsmuth, H. (2021). Syntopical graphs for computational argumentation tasks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (S. 1583-1595). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.acl-long.126
Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2021). CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021 Vorabveröffentlichung online. https://arxiv.org/abs/2110.02102
Biedenkapp, A., Rajan, R., Hutter, F., & Lindauer, M. (2021). TempoRL: Learning When to Act. In Proceedings of the international conference on machine learning (ICML) Vorabveröffentlichung online. https://arxiv.org/abs/2106.05262
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2021). Overview of Touché 2021: Argument Retrieval: Extended Abstract. In D. Hiemstra, M.-F. Moens, J. Mothe, R. Perego, M. Potthast, & F. Sebastiani (Hrsg.), Advances in Information Retrieval: 43rd European Conference on IR Research, Proceedings (S. 574-582). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12657 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72240-1_67
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2021). Overview of Touché 2021: Argument retrieval. In CLEF 2021 Working Notes: Proceedings of the Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum Bucharest, Romania, September 21st to 24th, 2021. (S. 2258-2284). (CEUR Workshop Proceedings; Band 2936). https://ceur-ws.org/Vol-2936/paper-205.pdf
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., & Hagen, M. (2021). Overview of Touché 2021: Argument Retrieval. In K. S. Candan, B. Ionescu, L. Goeuriot, H. Müller, A. Joly, M. Maistro, F. Piroi, G. Faggioli, & N. Ferro (Hrsg.), Experimental IR Meets Multilinguality, Multimodality, and Interaction. 12th International Conference of the CLEF Association (CLEF 2021) (Band 12880, S. 450-467). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-030-85251-1_28
Chen, W. F., Al-Khati, K., Stein, B., & Wachsmuth, H. (2021). Controlled Neural Sentence-Level Reframing of News Articles. In M.-F. Moens, X. Huang, L. Specia, & S. W.-T. Yih (Hrsg.), Findings of the Association for Computational Linguistics: EMNLP 2021 (S. 2683-2693). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-emnlp.228
Eggensperger, K., Müller, P., Mallik, N., Feurer, M., Sass, R., Awad, N., Lindauer, M., & Hutter, F. (2021). HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track) Vorabveröffentlichung online. https://arxiv.org/abs/2109.06716
Eimer, T., Biedenkapp, A., Reimer, M., Adriaensen, S., Hutter, F., & Lindauer, M. T. (2021). DACBench: A Benchmark Library for Dynamic Algorithm Configuration. In Z.-H. Zhou (Hrsg.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) (S. 1668-1674). (IJCAI International Joint Conference on Artificial Intelligence). https://doi.org/10.24963/ijcai.2021/230
Eimer, T., Benjamins, C., & Lindauer, M. T. (2021). Hyperparameters in Contextual RL are Highly Situational. In International Workshop on Ecological Theory of RL (at NeurIPS) https://doi.org/10.48550/arXiv.2212.10876
Eimer, T., Biedenkapp, A., Hutter, F., & Lindauer, M. (2021). Self-Paced Context Evaluation for Contextual Reinforcement Learning. In Proceedings of the international conference on machine learning (ICML) Vorabveröffentlichung online. https://www.tnt.uni-hannover.de/papers/data/1454/space.pdf
Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T., Deng, D., Lindauer, M., & Hutter, F. (2021). Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. In ICML 2021 Workshop AutoML Vorabveröffentlichung online. https://arxiv.org/abs/2105.01015
Gurcke, T., Alshomary, M., & Wachsmuth, H. (2021). Assessing the Sufficiency of Arguments through Conclusion Generation. In 8th Workshop on Argument Mining, ArgMining 2021 - Proceedings (S. 67-77). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.2110.13495
Hanselle, J., Tornede, A., Wever, M., & Hüllermeier, E. (2021). Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. In K. Karlapalem, H. Cheng, N. Ramakrishnan, R. K. Agrawal, P. K. Reddy, J. Srivastava, & T. Chakraborty (Hrsg.), Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings (S. 152-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12712 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-75762-5_13, https://doi.org/10.1007/978-3-030-75762-5_13
Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In ECML/PKDD workshop on Automating Data Science (ADS 2021) https://arxiv.org/abs/2109.04744
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). METHOD, DEVICE AND COMPUTER PROGRAM FOR PRODUCING A STRATEGY FOR A ROBOT. (Patent Nr. US2021008718).
Hutter, F., Fuks, L., Lindauer, M., & Awad, N. (2021). Verfahren, Vorrichtung und Computerprogramm zum Erstellen einer Strategie für einen Roboter. (Patent Nr. DE102019210372A1). Deutsches Patent- und Markenamt (DPMA). https://worldwide.espacenet.com/patent/search?q=pn%3DCN112215363A
Kadra, A., Lindauer, M., Hutter, F., & Grabocka, J. (2021). Well-tuned Simple Nets Excel on Tabular Datasets. In Proceedings of the international conference on Advances in Neural Information Processing Systems (NeurIPS 2021) Vorabveröffentlichung online. https://arxiv.org/abs/2106.11189
Kiesel, J., Spina, D., Wachsmuth, H., & Stein, B. (2021). The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases. In Proceedings of the 3rd Conference on Conversational User Interfaces, CUI 2021 Artikel 20 Association for Computing Machinery (ACM). https://doi.org/10.1145/3469595.3469615
